## Rows: 160
## Columns: 19
## $ location <chr> "Lancaster", "Lancaster", "Lancaster", "Lanc…
## $ year <dbl> 2019, 2019, 2019, 2019, 2019, 2019, 2019, 20…
## $ herb <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
## $ other <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
## $ trade <chr> "PRE only", "PRE only", "PRE only", "PRE onl…
## $ plot <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ trt <dbl> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4,…
## $ rep <dbl> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,…
## $ waterhempcontrol_14 <dbl> 85, 85, 95, 35, 99, 99, 99, 99, 91, 95, 85, …
## $ grasscontrol_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ waterhempcontrol_28 <dbl> 88, 25, 95, 10, 95, 99, 99, 99, 98, 88, 75, …
## $ grasscontrol_28 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ waterhempcontrol_harvest <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ counts_m2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ biomass_gm2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ yield_bu <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ phyto_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ dicambadrift_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ canopyclosure_14 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
| Name | data |
| Number of rows | 160 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| location | 0 | 1 | 8 | 9 | 0 | 2 | 0 |
| herb | 0 | 1 | 8 | 46 | 0 | 10 | 0 |
| other | 0 | 1 | 8 | 46 | 0 | 10 | 0 |
| trade | 0 | 1 | 5 | 14 | 0 | 10 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1.00 | 2019.50 | 0.50 | 2019.00 | 2019.00 | 2019.50 | 2020.00 | 2020.00 | ▇▁▁▁▇ |
| plot | 80 | 0.50 | 255.50 | 112.55 | 101.00 | 178.25 | 255.50 | 332.75 | 410.00 | ▇▇▁▇▇ |
| trt | 0 | 1.00 | 5.50 | 2.88 | 1.00 | 3.00 | 5.50 | 8.00 | 10.00 | ▇▇▇▇▇ |
| rep | 0 | 1.00 | 2.50 | 1.12 | 1.00 | 1.75 | 2.50 | 3.25 | 4.00 | ▇▇▁▇▇ |
| waterhempcontrol_14 | 0 | 1.00 | 92.71 | 12.59 | 35.00 | 92.00 | 99.00 | 99.00 | 100.00 | ▁▁▁▁▇ |
| grasscontrol_14 | 124 | 0.22 | 87.36 | 10.73 | 65.00 | 79.00 | 89.50 | 98.00 | 100.00 | ▂▃▃▃▇ |
| waterhempcontrol_28 | 0 | 1.00 | 85.47 | 19.62 | 0.00 | 80.00 | 94.00 | 99.00 | 100.00 | ▁▁▁▂▇ |
| grasscontrol_28 | 120 | 0.25 | 90.65 | 25.88 | 0.00 | 100.00 | 100.00 | 100.00 | 100.00 | ▁▁▁▁▇ |
| waterhempcontrol_harvest | 80 | 0.50 | 78.47 | 28.66 | 0.00 | 75.00 | 88.50 | 95.00 | 99.00 | ▁▁▁▂▇ |
| counts_m2 | 80 | 0.50 | 3.05 | 3.55 | 0.00 | 1.00 | 2.00 | 4.00 | 18.00 | ▇▂▁▁▁ |
| biomass_gm2 | 80 | 0.50 | 17.82 | 51.05 | 0.00 | 0.00 | 1.92 | 12.46 | 382.06 | ▇▁▁▁▁ |
| yield_bu | 52 | 0.68 | 59.21 | 11.57 | 30.44 | 49.23 | 62.93 | 68.64 | 74.80 | ▁▃▃▃▇ |
| phyto_14 | 85 | 0.47 | 1.93 | 2.81 | 0.00 | 0.22 | 1.00 | 3.00 | 12.00 | ▇▁▁▁▁ |
| dicambadrift_14 | 120 | 0.25 | 2.15 | 4.45 | 0.00 | 0.00 | 0.00 | 3.00 | 25.00 | ▇▁▁▁▁ |
| canopyclosure_14 | 120 | 0.25 | 63.38 | 12.69 | 20.00 | 60.00 | 70.00 | 70.00 | 77.00 | ▁▁▁▅▇ |
ggplot(data, aes(x=reorder(other,waterhempcontrol_14), y=waterhempcontrol_14,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")ggplot(data, aes(x=reorder(other,waterhempcontrol_28), y=waterhempcontrol_28,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")ggplot(data, aes(x=reorder(other,waterhempcontrol_harvest), y=waterhempcontrol_harvest,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none")ggplot(data, aes(x=reorder(other,biomass_gm2), y=biomass_gm2,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Weed biomass (g m2)") +
theme_minimal() +
theme(legend.position = "none")ggplot(data, aes(x=reorder(other,yield_bu), y=yield_bu,
fill=trade, color=trade)) +
geom_boxplot(color="black") +
geom_jitter(alpha=0.2) +
facet_grid(year ~ location) +
coord_flip() +
labs(x="", y="Waterhemp control (%)") +
theme_minimal() +
theme(legend.position = "none") # Data wrangling
new_dt <-
data %>%
rename (herbicide = other) %>%
mutate(
wt_14 = waterhempcontrol_14/100,
wt_28 = waterhempcontrol_28/100,
yield_kg = yield_bu * 67.5) %>%
mutate(
wt_14 =
case_when(
waterhempcontrol_14 == 100 ~ 0.99,
TRUE ~ wt_14),
wt_28 = case_when(
waterhempcontrol_28 == 100 ~ 0.99,
waterhempcontrol_28 == 0 ~ 0.01,
TRUE ~ wt_28)) %>%
filter(
herbicide != "PRE only"
)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: wt_14
## Chisq Df Pr(>Chisq)
## herbicide 15.4841 8 0.05039 .
## location 1.9251 1 0.16530
## year 9714.1367 1 < 2e-16 ***
## herbicide:location 6.4935 8 0.59213
## herbicide:year 1.4430 8 0.99361
## location:year 0.3624 1 0.54716
## herbicide:location:year 0.6957 8 0.99954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Model using herbicide trt as fixed, and rep, year, and location as random effects
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: wt_14
## Chisq Df Pr(>Chisq)
## herbicide 11.21 8 0.1901
CLD(lsmeans_14$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| herbicide | response | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 3 | PRE fb glufosinate + fomesafen + acetochlor | 0.9663473 | 0.0092718 | 131 | 0.9423191 | 0.9805724 | a |
| 9 | PRE fb glufosinate + fomesafen | 0.9644099 | 0.0096027 | 131 | 0.9396867 | 0.9792228 | a |
| 1 | PRE fb glufosinate + fomesafen + S-metolachlor | 0.9633468 | 0.0100886 | 131 | 0.9372445 | 0.9788374 | a |
| 2 | PRE fb glufosinate + pyroxasulfone | 0.9513317 | 0.0122489 | 131 | 0.9205233 | 0.9705792 | ab |
| 7 | PRE fb glufosinate + imazethapyr | 0.9491984 | 0.0129393 | 131 | 0.9165874 | 0.9694842 | ab |
| 8 | PRE fb glufosinate + acetochlor | 0.9470465 | 0.0131010 | 131 | 0.9142870 | 0.9677273 | ab |
| 6 | PRE fb glufosinate | 0.9426195 | 0.0139116 | 131 | 0.9080580 | 0.9646943 | ab |
| 4 | PRE fb glufosinate + S-metolachlor | 0.9408544 | 0.0142213 | 131 | 0.9056144 | 0.9634678 | ab |
| 5 | PRE fb glufosinate + dimethenamid-P | 0.9296513 | 0.0162142 | 131 | 0.8900147 | 0.9557144 | b |
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: wt_28
## Chisq Df Pr(>Chisq)
## herbicide 23.1383 10 0.010247 *
## location 14.2304 3 0.002608 **
## year 194.8150 1 < 2.2e-16 ***
## herbicide:location 13.3607 8 0.100027
## herbicide:year 2.3217 8 0.969526
## location:year 0.0097 1 0.921564
## herbicide:location:year 0.0139 8 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Model using herbicide trt as fixed, and rep, year, and location as random effects
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: wt_28
## Chisq Df Pr(>Chisq)
## herbicide 33.38 8 5.259e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CLD(lsmeans_28$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| herbicide | response | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 1 | PRE fb glufosinate + fomesafen | 0.9335527 | 0.0219066 | 131 | 0.8747880 | 0.9658156 | a |
| 3 | PRE fb glufosinate + fomesafen + S-metolachlor | 0.9316031 | 0.0228235 | 131 | 0.8702296 | 0.9651143 | a |
| 7 | PRE fb glufosinate + fomesafen + acetochlor | 0.9297294 | 0.0230599 | 131 | 0.8681042 | 0.9637635 | a |
| 9 | PRE fb glufosinate + acetochlor | 0.9012060 | 0.0304182 | 131 | 0.8227128 | 0.9471779 | ab |
| 8 | PRE fb glufosinate + pyroxasulfone | 0.8942952 | 0.0319956 | 131 | 0.8124210 | 0.9429429 | ab |
| 2 | PRE fb glufosinate + S-metolachlor | 0.8814474 | 0.0351589 | 131 | 0.7925916 | 0.9353419 | ab |
| 5 | PRE fb glufosinate + imazethapyr | 0.8442439 | 0.0431294 | 131 | 0.7391023 | 0.9120553 | bc |
| 6 | PRE fb glufosinate + dimethenamid-P | 0.8436929 | 0.0424061 | 131 | 0.7407439 | 0.9106904 | bc |
| 4 | PRE fb glufosinate | 0.7817276 | 0.0528757 | 131 | 0.6598743 | 0.8686175 | c |
library(ggridges)
new_dt %>%
ggplot(
aes(y=herbicide, x=yield_kg, fill=herbicide)) +
geom_density_ridges(scale=2, show.legend = FALSE)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## herbicide 428797 53600 8 67.094 0.5795 0.7912
## siteyr 35541129 17770564 2 67.950 192.1351 <2e-16 ***
## herbicide:siteyr 1318370 82398 16 67.087 0.8909 0.5820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## NOTE: Results may be misleading due to involvement in interactions
CLD(lsmeans_fit$emmeans, alpha=0.05, Letters=letters, adjust="none", reversed = TRUE) %>%
kbl() %>%
kable_classic_2(full_width = F)| siteyr | emmean | SE | df | lower.CL | upper.CL | .group | |
|---|---|---|---|---|---|---|---|
| 2 | Lan20 | 4672.755 | 84.84491 | 4.867462 | 4452.856 | 4892.654 | a |
| 1 | Bro19 | 4348.885 | 94.32107 | 6.946555 | 4125.503 | 4572.267 | b |
| 3 | Bro20 | 3311.142 | 84.84491 | 4.867462 | 3091.242 | 3531.041 | c |